The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automatic segmentation of echocardiographic sequences by active appearance motion models
Abstract
A novel extension of active appearance models (AAMs) for automated border detection in echocardiographic image sequences is reported. The active appearance motion model (AAMM) technique allows fully automated robust and time-continuous delineation of left ventricular (LV) endocardial contours over the full heart cycle with good results. Nonlinear intensity normalization was developed and employed to accommodate ultrasound-specific intensity distributions. The method was trained and tested on 16-frame phase-normalized transthoracic four-chamber sequences of 129 unselected infarct patients, split randomly into a training set (n = 65) and a test set (n = 64). Borders were compared to expert drawn endocardial contours. On the test set, fully automated AAMM performed well in 97% of the cases (average distance between manual and automatic landmark points was 3.3 mm, comparable to human interobserver variabilities). The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms. The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs.
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Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Cited by
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Noble, J. A., & Boukerroui, D. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.877092 · Cited by 1,157
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006
“In terms of segmentation, there are several examples of automated approaches to segmenting the LV, including the use of active appearance models 24 and a more recent example using deep learning.”
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Burton, J. O., Jefferies, H. J., Selby, N. M., & McIntyre, C. W. (2009) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03900808 · Cited by 686
Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI
Andreopoulos, A., & Tsotsos, J. K. (2008) · Medical Image Analysis · doi:10.1016/j.media.2007.12.003 · Cited by 360
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Metadata
- Authors
- Bosch, J. G., Mitchell, S. C., Lelieveldt, B. P. F., Nijland, F., Kamp, O., Sonka, M., & Reiber, J. H. C.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2002-11
- Volume
- 21
- Issue
- 11
- Pages
- 1374-1383
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2112691218
- Cited by
- 296
- References
- 1
- FWCI
- 15.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bosch, J. G., Mitchell, S. C., Lelieveldt, B. P. F., Nijland, F., Kamp, O., Sonka, M., & Reiber, J. H. C. (2002). Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2002.806427
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